Full-Stack Software Engineer, Reinforcement Learning

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role As a Full-Stack Software Engineer in RL, you'll build the platforms, tools, and interfaces that power environment creation, data collection, and training observability. The quality of Claude's next generation depends on the quality of the data we train it on — and the systems you build are what make that data possible. You'll own product surfaces end-to-end — from backend services and APIs to the web UIs that researchers, external vendors, and thousands of data labelers use every day. You don't need a background in ML research. What matters is that you can take an ambiguous, high-stakes problem and ship a polished, reliable product against it, fast. This team moves very quickly. Claude writes a lot of the code we commit, which means the bottleneck isn't typing — it's judgment, taste, and the ability to react to what researchers need next. You'll iterate on data collection strategies to distill the knowledge of thousands of human experts around the world into our models, and you'll do it in a loop that closes in hours and days, [... source excerpt omitted ...] fectively, advancing code generation through RL, pioneering fundamental RL research for large language models, and building the scalable training methodologies behind our frontier production models. The RL org is organized around four goals: solving the science of long-horizon tasks and continual learning, scaling RL data and environments to be comprehensive and diverse, automating software engineering end-to-end, and training the frontier production model. Our engineering teams build the environments, evaluation systems, data pipelines, and tooling that make all of this possible — from realistic agentic training environments and scalable code data generation to human data collect [... source excerpt omitted ...] mentation tooling so new vendors and internal users ramp up in hours, not weeks Partner closely with RL researchers, data operations, and vendor management to translate ambiguous requirements into well-scoped, well-designed products You May Be a Good Fit If You Have strong software engineering fundamentals and real full-stack range — you're comfortable owning a surface from database schema to frontend Are proficient in Python and a modern web stack (React, TypeScript, or similar) Have a track record of shipping systems that solved a hard problem, not just shipped on time — e.g. you built the thing that made your team 10x faster, or the internal tool nobody thought was possible